Due to popular demand, the deadline to submit a paper to the workshop is extended to September 23, 2016.

Stream Processing and Real-time analytics have caught the interest of the industry lately. Many use cases are nowadays waiting for relevant and efficient solutions to be developed. Such use cases include event-driven marketing, dynamic network management & optimization, real-time recommendation, context-aware applications and real-time fraud detection.

In the past years, researchers and practitioners in the area of data stream management [1, 2, 3] and Complex Event Processing (CEP) [4, 5, 6] have developed systems to process unbounded streams of data and quickly detect situations of interest.

Nowadays, big data technologies provide a new ecosystem to foster research in this area. Highly scalable distributed stream processors and the convergence of batch and stream engines (such as Apache Spark or Apache Flink) open new doors for highly scalable and distributed real-time analytics. Going further, those technologies also provide a solid foundation for real-time analytics algorithms that are complementary to the CEP in the use cases required by the industry. As a result, we also encourage submissions studying scalable on-line learning and incremental learning on stream processing infrastructure.

The workshop is an excellent opportunity to gather together actors from academia and industry to discuss, to explore and to refine new opportunities and use cases in the area. The workshop will benefit to both researchers and practitioners interested in the latest researches in real-time and stream processing. The workshop will showcase prototypes or products leveraging big data technologies as well as models and efficient algorithms for scalable complex event processors and context detection engines.